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...o Windows AD. My test environment is made of 2 Networks, which are connected through a VPN site2site tunnel. On one of this Networks I have the root domain (RD.LAN) with its DC rddc1.rd.and and a subdomain 2 (SD2.RD.LAN) with its DC sd2dc.sd2.rd.lan On the other network I have the subdomain 1 (SD1.RD.LAN) with its DC sd1dc1.sd1.rd.lan. I try the following steps on mserver1.sd1.rd.lan: first I install these required packages, as written in wiki.samba.org: build-essential libacl1-dev libattr1-dev libblkid-dev libgnutls-dev libreadline-dev python-dev python-dnspython gdb pkg-config libpopt...

Dear all: Assume I have 3 distributions, x1, x2, and x3. x1 ~ normal(mu1, sd1) x2 ~ normal(mu2, sd2) x3 ~ normal(mu3, sd3) y1 = x1 + x2 y2 = x1 + x3 Now that the data I can observed is only y1 and y2. It is easy to estimate (mu1+m2), (mu1+mu3), (sd1^2+sd2^2) and (sd1^2+sd3^2) by EM algorithm since y1 ~ normal(mu1+mu2, sqrt(sd1^2+sd2^2)) and y2 ~ normal(mu1+mu3, sqrt(sd1^2+s...

...9;t works, and, of course, I cannot authenticate on Linux using AD account. Any help would be welcome, I have to make this working by the end of the week. Regards =============================================================================== SMB.CONF [global] security = domain realm = SD1.COM password server = winsd1.sd1.com workgroup = SD1 winbind separator = + idmap uid = 10000-29999 idmap gid = 10000-29999 winbind enum users = yes winbind enum groups = yes template homedir = /home/%D/%U template shell = /bin/bash client use spnego = yes domain master = no ser...

I am doing very regular stuff like the following: attach(wtana) fm<- lm(Body.Wt.on.SD1~Heart.Wt, data=wtana) #fm<- lm(wtana$Body.Wt.on.SD1~wtana$Heart.Wt) lrf<- loess(Body.Wt.on.SD1~Heart.Wt, wtana) #lrf<- loess(wtana$Body.Wt.on.SD1~wtana$Heart.Wt) plot(Body.Wt.on.SD1,Heart.Wt) #plot(wtana$Body.Wt.on.SD1,wtana$Heart.Wt) #lines(spline(Heart.Wt,fitted(lrf)), col=2) abline(fm,...

...e two questions, (i) how is the function printing when there is no code to print and (ii) how is n1 which equals 1 being reported as 2? I suspect there is something fundamental about R that I don't know. Thank you for the help. John library(epicalc) n.for.2means.js <- function (mu1, mu2, sd1, sd2, ratio = 1, alpha = 0.05, power = 0.8) { n1 <- (sd1^2 + sd2^2/ratio) * (qnorm(1 - alpha/2) - qnorm(1 - power))^2/(mu1 - mu2)^2 n1 <- round(n1) n2 <- ratio * n1 if (length(alpha) == 1) { alpha1 <- NULL } else { alpha1 <- alpha } if (length(power) == 1) {...

...                 DEF = c(22,28,20,20,28,26,29,18,24,21),                                           GHI = c(32,27,32,36,37,37,34,23,25,32),                                           JKL = c(47,60,60,43,62,38,44,53,61,41)) sd_prices <- c(3.3483,3.9497,4.9721,9.3387)   # standard deviations say(sd1, sd2, sd3, sd4)                         weights <- c(0.10, 0.25, 0.20, 0.45)      # say (w1, w2, w3, w4) I need to calculate the standard deviation of the portfolio. The formula is stdev_portfolio = sqrt((w1*sd1)^2+(w2*sd2)^2+(w3*sd3)^2+(w4*sd4)^2 +                         2*w1*w2*sd1*sd2*cor...

...cision. I did this as seen in the code below and found that for some reason rgamma is giving many values equal to zero, which is strange since a gamma distribution is continuous. What is going on here? Thanks for any help in advance. Greg > x1 <- rgamma(10000, shape=0.001, scale=0.001) > sd1 <- 1/sqrt(x1) > truehist(sd1, xlim=c(0, 1.5)) Error in truehist(sd1, xlim = c(0, 1.5)) : 'nbins' must result in a positive integer > summary(sd1) Min. 1st Qu. Median Mean 3rd Qu. Max. 2.266e+01 9.311e+66 3.250e+153 Inf Inf...

Hello I have an main disk (operating sistem) and an spare disk for backup. Is it possible with rsinc command to copy whole disk n1 to disk2? Like: #rsinc -av /dev/sd0 /dev/sd1 thanks -- View this message in context: http://www.nabble.com/Rsync-to-spare-disk-tf2865622.html#a8008100 Sent from the Samba - rsync mailing list archive at Nabble.com.

Hi, I''m running Opensolaris 2009.06, and I''m facing a serious performance loss with ZFS ! It''s a raidz1 pool, made of 4 x 1TB SATA disks : zfs_raid ONLINE 0 0 0 raidz1 ONLINE 0 0 0 c7t2d0 ONLINE 0 0 0 c7t3d0 ONLINE 0 0 0 c7t4d0 ONLINE 0 0

hello, I would like to create the following barplot: I have 4 different data sets (same length + stddev for each data point) data1 sd1 data2 sd2 data3 sd3 data4 sd4 now, I'd like to plot in the following way: data1[1],data2[1],data3[1],data4[1] with it's sd-values side-by-side at one x-axis label (named "position 1") and each bar in different colors. data1[2],data2[2],data3[2],data4[2] at the next x-axis labe...

Hi, I already found a conversation on the integration of a normal distribution and two suggested solutions (https://stat.ethz.ch/pipermail/r-help/2007-January/124008.html): 1) integrate(dnorm, 0,1, mean = 0, sd = 1.2) and 2) pnorm(1, mean = 0, sd = 1.2) - pnorm(0, mean = 0, sd = 1.2) where the pnorm-approach is supposed to be faster and with higher precision. I want to integrate a mixed

...ha0)*f2 scatterplot3d(y[,1], y[,2], p , highlight.3d=TRUE, col.axis="blue", col.grid="lightblue",main="val iniz mistura normali multivariata", angle=120, pch=20) #verosimiglianza iniziale l0<-sum(log(p)) l1<-l0 alpha<-alpha0 mu1<-mu01 mu2<-mu02 sd1<-sd01 sd2<-sd02 for (iter in 1:itermax) { #passo E for (i in 1:n) { tau[i,1]<-(alpha*f1[i])/p[i] tau[i,2]<-((1-alpha)*f2[i])/p[i] } #passo M alpha= mean(tau[,1]) mu1=colSums(tau[,1]*y)/sum(tau[,1]) mu2=colSums(tau[,2]*y)/sum(tau[,2]) ycen1<-(y-mu1) ycen2<-(y-mu2) cov1<...

...ha0)*f2 scatterplot3d(y[,1], y[,2], p , highlight.3d=TRUE, col.axis="blue", col.grid="lightblue",main="val iniz mistura normali multivariata", angle=120, pch=20) #verosimiglianza iniziale l0<-sum(log(p)) l1<-l0 alpha<-alpha0 mu1<-mu01 mu2<-mu02 sd1<-sd01 sd2<-sd02 for (iter in 1:itermax) { #passo E for (i in 1:n) { tau[i,1]<-(alpha*f1[i])/p[i] tau[i,2]<-((1-alpha)*f2[i])/p[i] } #passo M alpha= mean(tau[,1]) mu1=colSums(tau[,1]*y)/sum(tau[,1]) mu2=colSums(tau[,2]*y)/sum(tau[,2]) ycen1<-(y-mu1) ycen2<-(y-mu2) cov1<...

...this a correct interpretation? Suspecting that perhaps this ''missing'' time was spent in I/O, I executed iosnoop while the program ran, with the following results: STRTIME ? ? ? ? ? ? ?DEVICE ?MAJ MIN ? UID ? PID D ? ?BLOCK ? SIZE ? ? ? ? ? ? ? ?PATHNAME ARGS 2010 Mar 18 09:22:55 sd1 ? ? ?32 ?13 10054 20116 R 32569904 ? 8192 /var/tmp/test-crystal/lib/cr/CrystalReportsRuntime.jar 2010 Mar 18 09:22:55 sd1 ? ? ?32 ?13 10054 20116 R 32569920 ? 8192 /var/tmp/test-crystal/lib/cr/CrystalReportsRuntime.jar 2010 Mar 18 09:22:56 sd1 ? ? ?32 ? 8 10054 20116 R 42673424 ? 8192 /usr/j2sdk-1....

...going series of datasets that have a bimodal distribution. I have used the following functions, in particular the ms() function, to estimate the parameters: two means, two standard deviations, and one proportion. Here is the code I've been using in S: btmp.bi <- function(vec, p, m1, m2, sd1, sd2) { (exp(p)/(1+exp(p)))*dnorm(vec,mean=m1,sd=abs(sd1))+ (1-(exp(p)/(1+exp(p))))*dnorm(vec,mean=m2,sd=abs(sd2)) } btmp11 <- ms( ~ - sum(log((btmp.bi(btmp1$Temp, p, m1, m2, s1, s2)))), start = list(p = 0.4, m1 = 38, m2 = 40, s1 = 1, s2...

...ing I could force in some space after the title. Here's working example where I'd like to make room for a legend. x <- rnorm(100) hist(x, freq=F, main="Different Meanings of Normality") lines(density(x)) xseq1 <- seq( min(x), max(x), length.out=100) m1 <- mean(x) sd1 <- sd(x) obsNormal <- dnorm(xseq1, mean=m1, sd=sd1) lines( xseq1, obsNormal, lty=2, col="red") truNormal <- dnorm(xseq1) lines(xseq1, truNormal, lty=3, col="green") legend(0,0.4, legend=c("observed density", "normal with observed mean & sd",...

...ntercepts and slopes int1 <- lapply(glsrun1, function(x) x$coefficient[1]) slo1 <- lapply(glsrun1, function(x) x$coefficient[2]) int2 <- lapply(glsrun2, function(x) x$coefficient[1]) slo2 <- lapply(glsrun2, function(x) x$coefficient[2]) # Step 6 Compute SD of intercepts and slopes int.sd1 <- sapply(glsrun1, function(x) x$coefficient[1]) slo.sd1 <- sapply(glsrun1, function(x) x$coefficient[2]) int.sd2 <- sapply(glsrun2, function(x) x$coefficient[1]) slo.sd2 <- sapply(glsrun2, function(x) x$coefficient[2]) cat("Original Standard Errors","\n", "Int...

...<-sm[i]*S1*(1-m1)*S2*m2 a31[i]<-sm[i]*S1*(1-m1)*S2*(1-m2)*S3 sum[i]<-a11[i]+a21[i]+a31[i] } #priors #priors are dbeta(0.5,0.5), uniform is dbeta(1,1) S1~dbeta(1,1)#I(0,0.2) # S2~dbeta(1,1)#I(0.15,0.5) # S3~dbeta(1,1)#I(0.3,1) m1~dbeta(1,1)#I(0.01,1) m2~dbeta(1,1)#I(0,1) sd ~ dunif(0,100) sd1 ~ dunif(0,100) sd2 ~ dunif(0,100) tau <- 1/(sd*sd) tau1 <- 1/(sd1*sd1) tau2 <- 1/(sd2*sd2) } model.file <- Model file.show(model.file) #The Data list(N = 4, sm=c(9309,3253,5292,1361), a1=c(16,3,4,2), a2=c(96,31,42,3), a3=c(47,5,18,1)) inits <- function(){ list(sd = 1000, sd1 = 1000,...

...<-sm[i]*S1*(1-m1)*S2*m2 a31[i]<-sm[i]*S1*(1-m1)*S2*(1-m2)*S3 sum[i]<-a11[i]+a21[i]+a31[i] } #priors #priors are dbeta(0.5,0.5), uniform is dbeta(1,1) S1~dbeta(1,1)#I(0,0.2) # S2~dbeta(1,1)#I(0.15,0.5) # S3~dbeta(1,1)#I(0.3,1) m1~dbeta(1,1)#I(0.01,1) m2~dbeta(1,1)#I(0,1) sd ~ dunif(0,100) sd1 ~ dunif(0,100) sd2 ~ dunif(0,100) tau <- 1/(sd*sd) tau1 <- 1/(sd1*sd1) tau2 <- 1/(sd2*sd2) } model.file <- Model file.show(model.file) #The Data list(N = 4, sm=c(9309,3253,5292,1361), a1=c(16,3,4,2), a2=c(96,31,42,3), a3=c(47,5,18,1)) inits <- function(){ list(sd = 1000, sd1 = 1000,...

...me plot. I need help to get the same x axis (centered) when overlay two plots (barplot2 and plotCI (or lineplot.CI) with errorbars), with diferent y axis. I use par (new=T) but the X axis names are not centered with the figures. An example of my data: mean1<-cbind(55.5,56.42,56.38,55.28) SD1<-cbind(10,9 ,7 ,3) x<-rbind(mean1,SD1) colnames(x)<-c("Autumn","Spring","Summer","Winter") rownames(x)<-c("MeanProts","SD") mean2<-cbind(10,15,9,8) SD2<-cbind(2,3 ,1 ,5) y<-rbind(mean2,SD2) colnames(y)<-c(&...